class_docstr: Calculate the confusion matrix of the model on the given dataset.
  
  Parameters
  ----------
  normalize_display : bool , default: True:
      boolean that determines whether to normalize the values of the matrix in the display.
  n_samples : int , default: 1_000_000
      number of samples to use for this check.
  random_state : int, default: 42
      random seed for all check internals.
code: def __init__(
      self,
      normalize_display: bool = True,
      n_samples: int = 1_000_000,
      random_state: int = 42,
      **kwargs,
  ):
      super().__init__(**kwargs)
      self.normalize_display = normalize_display
      self.n_samples = n_samples
      self.random_state = random_state
  
file_path: deepchecks/deepchecks/tabular/checks/model_evaluation/confusion_matrix_report.py
imports: import numpy
  from deepchecks.core import CheckResult
  from deepchecks.tabular import Context, SingleDatasetCheck
  from deepchecks.utils.abstracts.confusion_matrix_abstract import misclassified_samples_lower_than_condition, run_confusion_matrix_check
  from deepchecks.utils.strings import format_percent
label_desc: Use image node_id 1 to create a new ConfusionMatrixReport object from inherited base classes: SingleDatasetCheck with example: obj = ConfusionMatrixReport(normalize_display, n_samples, random_state)